Non-NAT Definite Diagnosis Models of COVID-19 Based on Hematological Features (preprint)

Background: Given that 2019 novel coronavirus (COVID-19) spreads rapidly, it is critical to make rapid and accurate detection of COVID-19 patients towards containment of SARS-CoV-2 virus. At present, COVID-19 patients are mainly identified through viral nuclear acid testing (NAT). However, factors such as time for patients being tested, experience of test operators, and specimen’s preparation, might affect the accuracy of testing results. The purpose of this study was to use different classification and feature selection methods to improve the diagnostic accuracy of COVID-19 patients. Methods: We utilized seven machine learning algorithms for assisting diagnosis of COVID-19 by developing a non-NAT algorithm. In order to reduce the number of input features while maintaining the models’ performance so as to decrease the cost and time consumption, we adopted three algorithms, such as Chi-square test, variance analysis, and feature importance tests to identify the optimal feature sets. Findings: The XGBoost and RF models displayed the best performance for COVID-19 detection, with the highest accuracy rate more than 0·96. The accuracy of RF model was 0·968 when using only ten hematological features and body temperature. Interpretation: Ten blood features and body temperature can fairly accurately determine whether a suspected patient is infected with COVID-19. Our model can improve the diagnostic accuracy of COVID-19 and reduce the spread. Funding: This work is supported by grants from the National Key Research and Development Program of China under Grant 2017YFE0123600, the Natural Science Foundation of China (81873931, 81974382 and 81773104), the Frontier Exploration Program of Huazhong University of Science and Technology (2015TS153), and the Major Scientific and Technological Innovation Projects in Hubei Province (2018ACA136).Declaration of Interests: All the authors stated that the paper had never been published elsewhere, and that there were no competing economic interests.Ethics Approval Statement: The collection, use, and retrospective analysis of chest CT images, CFs and SARS-CoV-2 nucleic acid PCR results of patients were approved by the institutional ethical committees of HUST-UH (IRB ID: [2020] IEC(A001)).

iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia (preprint)

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially reported in Wuhan, China since December, 2019. Here, we reported a timely and comprehensive resource named iCTCF to archive 256,356 chest computed tomography (CT) images, 127 types of clinical features (CFs), and laboratory-confirmed SARS-CoV-2 clinical status from 1170 patients, reaching a data volume of 38.2 GB. To facilitate COVID-19 diagnosis, we integrated the heterogeneous CT and CF datasets, and developed a novel framework of Hybrid-learning for UnbiaSed predicTion of COVID-19 patients (HUST-19) to predict negative cases, mild/regular and severe/critically ill patients, respectively. Although both CT images and CFs are informative in predicting patients with or without COVID-19 pneumonia, the integration of CT and CF datasets achieved a striking accuracy with an area under the curve (AUC) value of 0.978, much higher than that when exclusively using either CT (0.919) or CF data (0.882). Together with HUST- 19, iCTCF can serve as a fundamental resource for improving the diagnosis and management of COVID-19 patients.Authors Wanshan Ning, Shijun Lei, Jingjing Yang, and Yukun Cao contributed equally to this work.